An intensive 4-week industrial internship mastering Python, Pandas Data Cleaning, Machine Learning Algorithms, Deep Learning, Computer Vision, and Cloud API Deployment.
AUC-ROC Score: 0.942 • Precision: 91.8%
Inference Latency: 14ms • Batch Size: 64
Root Mean Squared Error (RMSE): 0.008
The Data Science & AI Internship at Money Mitra Network is a 4-week practical program covering Python 3.12, Pandas data wrangling, Scikit-Learn machine learning, and PyTorch model deployment. Interns complete real predictive modeling projects with verified dataset submission. Related tracks: explore our Python Engineering Internship and Full Stack Web Development programs.
Lead Data & AI Scientist
Ex-Senior ML Engineer focusing on predictive modeling, computer vision, and Python ecosystem pipelines.
Data Science is far more than just fitting model algorithms. Candidates master the full engineering pipeline from raw data extraction to continuous production API deployment.
Extract structured and unstructured data from SQL databases, RESTful JSON APIs, web scrapers, and CSV/Parquet files into Pandas DataFrames.
Clean missing values, detect outliers, analyze skewness, calculate correlation matrices, and generate statistical charts with Seaborn & Matplotlib.
Construct polynomial features, execute One-Hot & Target encoding, apply Standard Scaling, and select high-importance features using SHAP values.
Train baseline Linear/Logistic Regressions, Decision Trees, Ensembles (Random Forests, XGBoost, LightGBM), and PyTorch Neural Networks.
Optimize hyperparameters using GridSearch & Optuna. Evaluate performance using Confusion Matrices, ROC-AUC, F1-Scores, and RMSE metrics.
Package trained ML models using Pickle/Joblib, wrap them into fast FastAPI endpoints, and deploy interactive Streamlit dashboards.
Align your internship focus with industry roles across Data Engineering, Predictive Machine Learning, and Artificial Intelligence.
Master tabular data modeling, supervised/unsupervised algorithms, customer segmentation, recommendation engines, and time-series forecasting.
Construct artificial neural networks (ANNs), Convolutional Networks (CNNs) for image recognition, and Transformers for Natural Language Processing.
Build robust ETL data pipelines, write complex SQL queries, manage relational databases, and design automated analytics reporting systems.
Master the exact software stack used by Data Scientists and AI Engineers globally.
The premier language for modern AI. Master list comprehensions, lambda functions, OOP, and numerical optimization.
High-performance vector operations, DataFrame manipulation, merging, grouping, pivot tables, and time-series indexing.
The standard ML library for regression, classification, clustering, preprocessing pipelines, and cross-validation.
Facebook AI's deep learning framework. Build dynamic neural network graphs, custom loss functions, and CUDA GPU tensors.
Extreme Gradient Boosting algorithms designed for winning Kaggle competitions and handling enterprise tabular datasets.
Create informative data visualizations including heatmaps, pairplots, distribution curves, and ROC curves.
You will write clean, production-ready Python code. From vectorizing loops in NumPy to wrapping ML models in Scikit-Learn Pipelines and serving predictions via FastAPI.
Below is a sample Jupyter Notebook cell demonstrating model initialization, training, and evaluation.
# Import Core Data Science Libraries
import pandas as pd
from xgboost import XGBClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
# Load & Split Clean Dataset
df = pd.read_csv("customer_churn.csv")
X_train, X_test, y_train, y_test = train_test_split(df.drop('churn', axis=1), df['churn'])
# Instantiate & Fit XGBoost Classifier
model = XGBClassifier(n_estimators=200, learning_rate=0.05)
model.fit(X_train, y_train)
# Evaluate Model Performance
auc = roc_auc_score(y_test, model.predict_proba(X_test)[:, 1])
print(f"Model AUC-ROC Score: {auc:.4f}")
A step-by-step path from data wrangling basics to advanced AI model deployment.
Master Python 3.12 data structures, NumPy vector arrays, Pandas DataFrames, handling null values, feature transformation, and exploratory data visualization with Seaborn.
Understand the mathematical foundations of Linear Regression, Logistic Regression, Decision Trees, and K-Nearest Neighbors using Scikit-Learn.
Master Ensemble methods (Bagging & Boosting). Build Random Forests and XGBoost models, followed by building multi-layer Artificial Neural Networks in PyTorch.
Serialize your trained ML models into Joblib artifacts, build RESTful prediction APIs using FastAPI, and construct an interactive Streamlit web dashboard.
Build an impressive, portfolio-ready Data Science GitHub repository.
Build an XGBoost model on Telecom data to predict customer churn with over 90% accuracy.
Perform exploratory analysis on 500k sales records to discover revenue drivers and seasonality.
Train Logistic Regression and Decision Tree models to score loan applicant risk probabilities.
Apply Ridge and Lasso Regression with feature engineering to predict residential property prices.
Forecast stock prices and sales volume using ARIMA and PyTorch LSTM recurrent neural networks.
Train a Convolutional Neural Network (CNN) in PyTorch to classify medical images with high precision.
Wrap a trained ML model in a high-speed FastAPI web server supporting batch JSON inference requests.
Build an interactive web application using Streamlit that allows users to upload CSVs and view live AI predictions.
Earn performance-based stipends while building verifiable proof of your AI engineering capabilities.
Awarded to candidates who successfully complete all 8 core lab projects with high technical accuracy.
Awarded for top 15% performance, including high-accuracy models and clean FastAPI code repos.
Awarded to top 5% candidates demonstrating outstanding model architectures, Streamlit apps, or research reports.
Submit your candidate application via our official admissions form to reserve your slot in the upcoming 2026 enterprise cohort.
Open Official Application Form (Google Form) →Everything you need to know about joining our Data Science & AI Cohort.